Sentiment Analysis of the Covid-19 Virus Infection in Indonesian Public Transportation on Twitter Data: A Case Study of Commuter Line Passengers

Intania Cahya Sari, Yova Ruldeviyani

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The appearance of the Covid-19 virus in early 2020 became a frightening pandemic for the world, including Indonesia. The infection of the Covid-19 virus was rapid because of its transmission can be through human contact. This condition causes worrying in society. Besides, these worrying also occurs in the passenger of public transportation, especially the commuter line. Passengers in large numbers and push each other will cause worry if commuter line passengers will transmit the Covid-19 virus to the commuter line. Many passengers write their opinions about the transmission of the Covid-19 pandemic on social media Twitter. This causes various opinions that can be positive, negative, or even neutral. Therefore, to see the opinions on commuter line passengers, a research was made to analyze the sentiment of the Covid-19 transmission to commuter line passengers. This research was implemented using a comparison of 2 methods, Naïve Bayes outperformed the Decision Tree with an accuracy of 73.59%. Furthermore, the result of sentiment analysis was a positive classification compared to the other 2 classes.

Original languageEnglish
Title of host publication2020 International Workshop on Big Data and Information Security, IWBIS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages23-28
Number of pages6
ISBN (Electronic)9781728190983
DOIs
Publication statusPublished - 17 Oct 2020
Event5th International Workshop on Big Data and Information Security, IWBIS 2020 - Depok, Indonesia
Duration: 17 Oct 202018 Oct 2020

Publication series

Name2020 International Workshop on Big Data and Information Security, IWBIS 2020

Conference

Conference5th International Workshop on Big Data and Information Security, IWBIS 2020
CountryIndonesia
CityDepok
Period17/10/2018/10/20

Keywords

  • Covid-19
  • Decision Tree
  • Naïve Bayes
  • Sentiment Analysis

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